Overview

A straightforward way of speeding up your analysis is to buy a better
computer. Modern desktops are relatively cheap, especially compared to
user time. However, it isn’t clear if upgrading your computing is worth
the cost. The benchmarkme package provides a set of benchmarks to
help quantify your system. More importantly, it allows you to compare
your timings with other
systems.

The benchmark_std() function

This benchmarks numerical operations such as loops and matrix
operations. This benchmark comprises of three separate benchmarks:
prog, matrix_fun, and matrix_cal. If you have less than 3GB of RAM
(run get_ram() to find out how much is available on your system), then
you should kill any memory hungry applications, e.g. firefox, and set
runs = 1 as an argument.

To benchmark your system, use

library("benchmarkme")

## Increase runs if you have a higher spec machine

res = benchmark_std(runs = 3)

and upload your results

## You can control exactly what is uploaded. See details below.

upload_results(res)

You can compare your results to other users
via

plot(res)

The benchmark_io() function

This function benchmarks reading and writing a 5MB or 50MB (if you have
less than 4GB of RAM, reduce the number of runs to 1). Run the
benchmark using

res_io = benchmark_io(runs = 3)

upload_results(res_io)

plot(res_io)

By default the files are written to a temporary directory generated

tempdir()

which depends on the value of

Sys.getenv("TMPDIR")

You can alter this to via the tmpdir argument. This is useful for
comparing hard drive access to a network drive.

res_io = benchmark_io(tmpdir = "some_other_directory")

Parallel benchmarks

The benchmark functions above have a parallel option - just simply
specify the number of cores you want to test. For example to test using
four cores

res_io = benchmark_std(runs = 3, cores = 4)

Previous versions of the package

This package was started around 2015. However, multiple changes in the
byte compiler over the last few years, has made it very difficult to use
previous results. So we have to start from scratch.

The previous data can be obtained via

data(past_results, package = "benchmarkmeData")

Machine specs

The package has a few useful functions for extracting system specs:

RAM: get_ram()

CPUs: get_cpu()

BLAS library: get_linear_algebra()

Is byte compiling enabled: get_byte_compiler()

General platform info: get_platform_info()

R version: get_r_version()

The above functions have been tested on a number of systems. If they
don’t work on your system, please raise
GitHub issue.

Uploaded data sets

A summary of the uploaded data sets is available in the
benchmarkmeData
package

data(past_results_v2, package = "benchmarkmeData")

A column of this data set, contains the unique identifier returned by
the upload_results() function.

What’s uploaded

Two objects are uploaded:

Your benchmarks from benchmark_std or benchmark_io;

A summary of your system information (get_sys_details()).

The get_sys_details() returns:

Sys.info();

get_platform_info();

get_r_version();

get_ram();

get_cpu();

get_byte_compiler();

get_linear_algebra();

installed.packages();

Sys.getlocale();

The benchmarkme version number;

Unique ID - used to extract results;

The current date.

The function Sys.info() does include the user and nodenames. In the
public release of the data, this information will be removed. If you
don’t wish to upload certain information, just set the corresponding
argument, i.e.